@string { IJCV = "International Journal of Computer Vision (IJCV)" } 
@string { CVPR = "IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)" } 
@string { NIPS = "Advances in Neural Information Processing Systems (NeurIPS)" } 
@string { NIPSpublisher = "Curran Associates, Inc." } 
@string { ICCV = "IEEE International Conference on Computer Vision (ICCV)" } 
@string { ICML = "International Conference on Machine Learning (ICML)" } 
@string { PMLR = "Proceedings of Machine Learning Research"}
@string { ICLR = "International Conference on Learning Representations (ICLR)" } 
@string { ECCV = "European Conference on Computer Vision (ECCV)" } 
@string { TIP  = "IEEE Transactions on Image Processing (TIP)" } 
@string { BMVC = "British Machine Vision Virtual Conference (BMVC)" } 
@string { TPAMI= "IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)" } 
@string { UAI  = "Uncertainty in Artificial Intelligence (UAI)"}
@string { TNNLS= "IEEE Transactions on Neural Networks and Learning Systems (TNNLS)"}
@string { KDD  = "ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)"}
@string { ICIP = "IEEE International Conference on Image Processing"}
@string { ACMMM= "ACM International Conference on Multimedia (ACM MM)"}

@article{Layout2image_OWA,
  title={Layout2image: Image Generation from Layout},
  author={Zhao, Bo and Yin, Weidong and Meng, Lili and Sigal, Leonid},
  journal=IJCV,
  pages={1--18},
  year={2020},
  publisher={Springer}
}

@inproceedings{Layout2image,
  title={Image generation from layout},
  author={Zhao, Bo and Meng, Lili and Yin, Weidong and Sigal, Leonid},
  booktitle=CVPR,
  pages={8584--8593},
  year={2019}
}

@InProceedings{coco,
  author = {Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio},
  title = {{COCO-Stuff}: Thing and Stuff Classes in Context},
  booktitle = CVPR,
  month = {June},
  year = {2018}
} 

@article{vg,
  title={{Visual Genome}: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
  author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and others},
  journal=IJCV,
  volume={123},
  number={1},
  pages={32--73},
  year={2017},
  publisher={Springer}
}


@inproceedings{StyleGAN,
  title={A style-based generator architecture for generative adversarial networks},
  author={Karras, Tero and Laine, Samuli and Aila, Timo},
  booktitle=CVPR,
  pages={4401--4410},
  year={2019}
}

@inproceedings{pix2pix,
  title={Image-to-image translation with conditional adversarial networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  booktitle=CVPR,
  pages={1125--1134},
  year={2017}
}

@inproceedings{pix2pixHD,
  title={High-Resolution Image Synthesis and Semantic Manipulation With Conditional GANs},
  author={Wang, Ting-Chun and Liu, Ming-Yu and Zhu, Jun-Yan and Tao, Andrew and Kautz, Jan and Catanzaro, Bryan},
  booktitle=CVPR,
  month={June},
  year={2018}
}

@inproceedings{ResNet,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle=CVPR,
  pages={770--778},
  year={2016}
}

@inproceedings{SNGAN,
title={Spectral Normalization for Generative Adversarial Networks},
author={Takeru Miyato and Toshiki Kataoka and Masanori Koyama and Yuichi Yoshida},
booktitle=ICLR,
year={2018},
}

@inproceedings{LostGANv1,
  title={Image synthesis from reconfigurable layout and style},
  author={Sun, Wei and Wu, Tianfu},
  booktitle=ICCV,
  pages={10531--10540},
  year={2019}
}


@ARTICLE{LostGANv2,
  author={Sun, Wei and Wu, Tianfu},
  journal=TPAMI, 
  title={Learning Layout and Style Reconfigurable {GANs} for Controllable Image Synthesis}, 
  year={2021},
  doi={10.1109/TPAMI.2021.3078577}}


@inproceedings{SOAR,
  title={Specifying object attributes and relations in interactive scene generation},
  author={Ashual, Oron and Wolf, Lior},
  booktitle=ICCV,
  pages={4561--4569},
  year={2019}
}

@inproceedings{Canonical_Representations,
  author          = {Roei Herzig and Amir Bar and Huijuan Xu and Gal Chechik and Trevor Darrell and Amir Globerson},
  title           = {Learning Canonical Representations for Scene Graph to Image Generation},
  booktitle       = ECCV,
  year            = {2020},
  publisher={Springer}
}

@inproceedings{OCGAN,
  title={Object-centric image generation from layouts},
  author={Tristan Sylvain and Pengchuan Zhang and Yoshua Bengio and R. Devon Hjelm and Shikhar Sharma},
  booktitle=ICLR,
  year={2021}
}

@inproceedings{sg2im,
  title={Image generation from scene graphs},
  author={Johnson, Justin and Gupta, Agrim and Fei-Fei, Li},
  booktitle=CVPR,
  pages={1219--1228},
  year={2018}
}

@inproceedings{IS,
  title={Improved techniques for training {GANs}},
  author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
  booktitle=NIPS,
  pages={2234--2242},
  year={2016},
  publisher = {Curran Associates, Inc.},
}

@inproceedings{FID,
  title={{GANs} Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium},
  author={Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
  booktitle=NIPS,
  pages={6626--6637},
  publisher = {Curran Associates, Inc.},
  year={2017}
}

@inproceedings{LIPIS,
  title={The unreasonable effectiveness of deep features as a perceptual metric},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A and Shechtman, Eli and Wang, Oliver},
  booktitle=CVPR,
  pages={586--595},
  year={2018}
}


@inproceedings{CAS,
  title={Classification accuracy score for conditional generative models},
  author={Ravuri, Suman and Vinyals, Oriol},
  booktitle=NIPS,
  pages={12247--12258},
  publisher = {Curran Associates, Inc.},
  year={2019}
}


@article{BachGAN,
  title={{BachGAN}: High-Resolution Image Synthesis From Salient Object Layout},
  author={Yandong Li and Y. Cheng and Zhe Gan and Licheng Yu and Liqiang Wang and Jing-jing Liu},
  journal=CVPR,
  year={2020},
  pages={8362-8371}
}

@inproceedings{COLOR,
  title={Scene graph to image generation with contextualized object layout refinement},
  author={Maor Ivgi and Yaniv Benny and Avichai Ben-David and Jonathan Berant and Lior Wolf},
  booktitle=ICIP,
  year={2021},
  pages={2428-2432},
  doi={10.1109/ICIP42928.2021.9506651}
}


@inproceedings{ResNet_preact,
  author          = {Kaiming He and X. Zhang and Shaoqing Ren and Jian Sun},
  title           = {Identity mappings in deep residual networks},
  booktitle       = ECCV,
  year            = {2016},
  pages           = {630--646},
  publisher={Springer}
}

@article{YOLOv4,
  title={YOLOv4: optimal speed and accuracy of object detection},
  author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.10934}
}

@article{GN,
  title={Group Normalization},
  author={Yuxin Wu and Kaiming He},
  journal=IJCV,
  year={2019},
  volume={128},
  pages={742-755},
  publisher={Springer}
}

@InProceedings{BN,
  title = 	 {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
  author = 	 {Sergey Ioffe and Christian Szegedy},
  booktitle =  ICML,
  pages = 	 {448--456},
  year = 	 {2015},
  publisher       = {PMLR}
}


@inproceedings{SPADE,
  title={Semantic image synthesis with spatially-adaptive normalization},
  author={Park, Taesung and Liu, Ming-Yu and Wang, Ting-Chun and Zhu, Jun-Yan},
  booktitle=CVPR,
  pages={2337--2346},
  year={2019}
}

@article{CLADE,
  title={Rethinking spatially-adaptive normalization},
  author={Zhentao Tan and Dongdong Chen and Qi Chu and Menglei Chai and Jing Liao and Mingming He and Lu Yuan and Nenghai Yu},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.02867}
}

@inproceedings{Attention,
author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
title = {Attention is all you need},
year = {2017},
publisher = {Curran Associates Inc.},
booktitle = NIPS,
pages = {6000–6010},
numpages = {11},
}

@InProceedings{SAGAN,
  title = {Self-attention generative adversarial networks},
  author = {Zhang, Han and Goodfellow, Ian and Metaxas, Dimitris and Odena, Augustus},
  pages = {7354--7363},
  year = {2019},
  booktitle=ICML,
  publisher = {PMLR}
} 

@article{SAST,
  title={Arbitrary style transfer with style-attentional networks},
  author={Dae Young Park and Kwang Hee Lee},
  journal=CVPR,
  year={2019},
  pages={5873-5881}
}

@inproceedings{CCFPSE,
  title={Learning to predict layout-to-image conditional convolutions for semantic image synthesis},
  author={Xihui Liu and Guojun Yin and Jing Shao and Xiaogang Wang and Hongsheng Li},
  publisher = {Curran Associates Inc.},
  booktitle = NIPS,
  year={2019},
  pages           = {570--580},
}

% 简直了，用的还是SPADE之前的方法改的
@ARTICLE{DCIS,
  author={Zichen {Yang} and Haifeng {Liu} and Deng {Cai}},
  journal=TIP, 
  title={On the Diversity of Conditional Image Synthesis with Semantic Layouts}, 
  year={2019},
  volume={28},
  number={6},
  pages={2898-2907},
  doi={10.1109/TIP.2019.2891935}}
}

@inproceedings{SEAN,
  title={{SEAN}: Image Synthesis with Semantic Region-Adaptive Normalization},
  author={Zhu, Peihao and Abdal, Rameen and Qin, Yipeng and Wonka, Peter},
  booktitle=CVPR,
  pages={5104--5113},
  year={2020}
}

@inproceedings{RetrieveGAN,
  title={{RetrieveGAN}: Image Synthesis via Differentiable Patch Retrieval},
  author={Tseng, Hung-Yu and Lee, Hsin-Ying and Jiang, Lu and Yang, Ming-Hsuan and Yang, Weilong},
  booktitle=ECCV,
  year={2020},
  publisher={Springer}
}

@inproceedings{BIN,
  title={Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks},
  author={Hyeonseob Nam and Hyo-Eun Kim},
  booktitle=NIPS,
  publisher = {Curran Associates Inc.},
  pages           = {2558--2567},
  year={2018}
}

@inproceedings{MaskRCNN,
  title={Mask {R-CNN}},
  author={He, Kaiming and Gkioxari, Georgia and Doll{\'a}r, Piotr and Girshick, Ross},
  booktitle=ICCV,
  pages={2961--2969},
  year={2017}
}

@inproceedings{cGANProject,
  title={{cGANs} with Projection Discriminator},
  author={Takeru Miyato and Masanori Koyama},
  booktitle=ICLR,
  year={2018},
}

@inproceedings{ADAM,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik and Ba, Jimmy},
  booktitle=ICLR,
  year={2014}
}


@incollection{PyTorch,
title = {{PyTorch}: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = NIPS,
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.}
}


@incollection{skipinit,
title = {Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks},
author = {Soham De and Samuel L. Smith},
booktitle = NIPS,
year = {2020},
publisher = {Curran Associates, Inc.}
}
% 去掉BN加上alpha

@inproceedings{ReZero,
  title={{ReZero} is All You Need: Fast Convergence at Large Depth},
  author={Thomas Bachlechner and Bodhisattwa Prasad Majumder and Huanru Henry Mao and Garrison Cottrell and Julian McAuley},
  booktitle=UAI,
  year={2021},
  publisher=PMLR,
}

@inproceedings{GAN,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  booktitle=NIPS,
  pages={2672--2680},
  year={2014},
  publisher = {Curran Associates, Inc.}
}

@article{DAGAN,
  title={Dual Attention GANs for Semantic Image Synthesis},
  author={Hao Tang and Song Bai and Nicu Sebe},
  journal={ACM International Conference on Multimedia (MM)},
  pages={1994--2002},
  year={2020}
}

@inproceedings{Layout_Generation_and_Completion_with_Self-attention,
  title={Layout Generation and Completion with Self-attention},
  author={Gupta, Kamal and Achille, Alessandro and Lazarow, Justin and Davis, Larry and Mahadevan, Vijay and Shrivastava, Abhinav},
  journal={arXiv preprint arXiv:2006.14615},
  year={2020}
}
% layout transformer 生成 layout，同时试着生成图片

@article{LayoutGAN,
  title={{LayoutGAN}: Generating Graphic Layouts with Wireframe Discriminators},
  author={Jianan Li and Jimei Yang and Aaron Hertzmann and Jianming Zhang and Tingfa Xu},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.06767}
}


@inproceedings{AlexNet,
  author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
  title = {ImageNet Classification with Deep Convolutional Neural Networks},
  year = {2012},
  publisher = {Curran Associates Inc.},
  booktitle = NIPS,
  pages = {1097–1105},
}

@inproceedings{Attribute-guided_image_generation_from_layout,
  author          = {Ke Ma and Bo Zhao and Leonid Sigal},
  title           = {Attribute-guided image generation from layout},
  booktitle       = BMVC,
  year            = {2020},
  pages           = {0384:1--13},
  publisher       = {British Machine Vision Virtual Association},
}
% layout2im基础上加attr

@inproceedings{FID_smaller_with_more_samples,
  title={Demystifying {MMD} {GANs}},
  author={Bi{\'n}kowski, Miko{\l}aj and Sutherland, Dougal J. and Arbel, Michael and Gretton, Arthur},
  booktitle=ICLR,
  year={2018}
}

@article{Inferring_Semantic_Layout_for_Hierarchical_Text-to-Image_Synthesis,
  title={Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis},
  author={Seunghoon Hong and Dingdong Yang and Jongwook Choi and H. Lee},
  journal=CVPR,
  year={2018},
  pages={7986-7994}
}
% 被CVPR审稿人要求引用的

@article{Object-Driven_Text-To-Image_Synthesis_via_Adversarial_Training,
  title={Object-Driven Text-To-Image Synthesis via Adversarial Training},
  author={Wenbo Li and Pengchuan Zhang and Lei Zhang and Qiuyuan Huang and Xiaodong He and Siwei Lyu and Jianfeng Gao},
  journal=CVPR,
  year={2019},
  pages={12166-12174}
}
% 上面的后续，同样在text2img中嵌入了layout2img


@article{Generating_unseen_complex_scenes_are_we_there_yet,
  title={Generating unseen complex scenes: are we there yet?},
  author={Arantxa Casanova and Michal Drozdzal and Adriana Romero-Soriano},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.04027}
}

@article{TPAMI2020_pathway,
  author={Tobias Hinz and Stefan Heinrich and Stefan Wermter},
  journal=TPAMI, 
  title={Semantic Object Accuracy for Generative Text-to-Image Synthesis}, 
  year={2020},
  doi={10.1109/TPAMI.2020.3021209}
}

@inproceedings{ICLR2019_pathway,
  title={Generating Multiple Objects at Spatially Distinct Locations},
  author={Tobias Hinz and Stefan Heinrich and Stefan Wermter},
  booktitle=ICLR,
  year={2019}
}



@article{object_compositionality_NN2020_google,
	Author = {Sjoerd {van Steenkiste} and Karol Kurach and J{\"u}rgen Schmidhuber and Sylvain Gelly},
	Journal = {Neural Networks},
	Pages = {309-325},
	Title = {Investigating object compositionality in Generative Adversarial Networks},
	Volume = {130},
	Year = {2020},
  Publisher ={Elsevier}
	}
% 使用attention考虑物体之间的关系，逐个生成后compose起来，主要在synthetic的场景实验


@inproceedings{ConvLSTM,
	Author = {Shi, Xingjian and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-kin and WOO, Wang-chun},
	Booktitle = NIPS,
	Publisher = {Curran Associates, Inc.},
	Title = {Convolutional {LSTM} Network: A Machine Learning Approach for Precipitation Nowcasting},
	Volume = {28},
	Year = {2015},
  pages = {1-9}
  }

@inproceedings{YOLOv1,
  title={You Only Look Once: Unified, Real-Time Object Detection},
  author={Joseph Redmon and Santosh Divvala and Ross Girshick and Ali Farhadi},
  booktitle= CVPR,
  year={2016},
  pages={779-788}
}

@article{Deep_Consensus_Learning,
  title={Deep Consensus Learning},
  author={Wei Sun and Tianfu Wu},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.08475}
}

@article{Zhou2020BatchGN,
  title={Batch Group Normalization},
  author={Xiaoyun Zhou and Jiacheng Sun and Nanyang Ye and X. Lan and Q. Luo and Bolin Lai and Pedro M. Esperança and G. Yang and Zhenguo Li},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.02782}
}



@inproceedings{spectral_bias,
  title = 	 {On the Spectral Bias of Neural Networks},
  author = 	 {Nasim Rahaman and Aristide Baratin and Devansh Arpit and Felix Draxler and Min Lin and Fred A. Hamprecht and Yoshua Bengio and
Aaron Courville },
  booktitle =  ICML,
  pages = 	 {448--456},
  year = 	 {2019},
  publisher       = {PMLR}
}

@techreport{cifar10,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Nair, Vinod and Hinton, Geoffrey},
  year={2009},
  institution = {Department of Computer Science, University of Toronto},
}

@article{FB_GAN,
  title={Spatial Frequency Bias in Convolutional Generative Adversarial Networks},
  author={Mahyar Khayatkhoei and Ahmed Elgammal},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.01473}
}


@inproceedings{VAE,
  title={Auto-encoding variational bayes},
  author={Kingma, Diederik P. and Welling, Max},
  booktitle=ICLR,
  year={2014}
}


@inproceedings{betaVAE,
  title={{$\beta$}-{VAE}: Learning basic visual concepts with a constrained variational framework},
  author={Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander},
  booktitle=ICLR,
  year={2017}
}

@article{CRN,
  title={Photographic Image Synthesis with Cascaded Refinement Networks},
  author={Qifeng Chen and Vladlen Koltun},
  journal=ICCV,
  year={2017},
  pages={1520-1529}
}

@article{ResNext,
  title={Aggregated Residual Transformations for Deep Neural Networks},
  author={Saining Xie and Ross Girshick and Piotr Doll{\'a}r and Zhuowen Tu and Kaiming He},
  journal=CVPR,
  year={2017},
  pages={5987-5995}
}

@inproceedings{Improved_precision_recall,
  title={Improved Precision and Recall Metric for Assessing Generative Models},
  author={T. Kynk{\"a}{\"a}nniemi and Tero Karras and Samuli Laine and Jaakko Lehtinen and Timo Aila},
  booktitle=NIPS,
  year={2019},

}

@inproceedings{Discovering_Causal_Signals_in_Images,
  title={Discovering Causal Signals in Images},
  author={David Lopez-Paz and Robert Nishihara and Soumith Chintala and B. Sch{\"o}lkopf and Le on Bottou},
  booktitle=CVPR,
  year={2017},
  pages={58-66}
}


@article{TNNLS2019Entropy,
  author={Xie, Feng and Cai, Ruichu and Zeng, Yan and Gao, Jiantao and Hao, Zhifeng},  
  journal= TNNLS,
  title={An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models With IID Noise Variables},
  year={2020},  
  volume={31},  
  number={5},  
  pages={1667-1680},  
  doi={10.1109/TNNLS.2019.2921613}
}



@incollection{l1_soft_threshold,
title = {Sparsity-Aware Learning: Concepts and Theoretical Foundations},
editor = {Sergios Theodoridis},
booktitle = {Machine Learning},
publisher = {Academic Press},
edition = {Second Edition},
pages = {427-472},
year = {2020},
isbn = {978-0-12-818803-3},
doi = {https://doi.org/10.1016/B978-0-12-818803-3.00019-2},
url = {https://www.sciencedirect.com/science/article/pii/B9780128188033000192},
author = {Sergios Theodoridis},
}

@article{ADMM,
  title={Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers},
  author={Stephen Boyd and Neal Parikh and Eric Chu and Borja Peleato and Jonathan Eckstein},
  journal={Foundation and Trends in Machine Learning},
  year={2011},
  volume={3},
  pages={1-122}
}

@article{FasterRCNN,
  title={{Faster R-CNN}: Towards Real-Time Object Detection with Region Proposal Networks},
  author={Shaoqing Ren and Kaiming He and Ross B. Girshick and Jian Sun},
  journal=TPAMI,
  year={2015},
  volume={39},
  pages={1137-1149}
}

@inproceedings{ILeadYouHelp,
author = {Oh, Changhoon and Song, Jungwoo and Choi, Jinhan and Kim, Seonghyeon and Lee, Sungwoo and Suh, Bongwon},
title = {I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence},
year = {2018},
isbn = {9781450356206},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3173574.3174223},
doi = {10.1145/3173574.3174223},
booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},
pages = {1–13},
numpages = {13},
keywords = {human computer collaboration, human-ai interaction, artificial intelligence},
location = {Montreal QC, Canada},
series = {CHI '18}
}


@inproceedings{LAMA,
author = {Zejian Li and Jingyu Wu and Immanuel Koh and Yongchuan Tang and Lingyun Sun},
title = {Image Synthesis from Layout with Locality-Aware Mask Adaption},
year = {2021},
publisher = {IEEE},
booktitle = ICCV
}

@article{Jahn2021HighResolutionCS,
  title={High-Resolution Complex Scene Synthesis with Transformers},
  author={Manuel Jahn and Robin Rombach and Bj{\"o}rn Ommer},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.06458}
}
% transformer layout2im，德国团队那个

@book{ECI,
  title={Elements of Causal Inference: Foundations and Learning Algorithms},
  author={Jonas Peters and Dominik Janzing and Bernhard Sch{\"o}lkopf},
  publisher       = {MIT Press},
  year={2017}
}

@book{Causality,
  title={Causality: Models, Reasoning and Inference},
  author={Judea Pearl},
  publisher       = {Cambridge University Press},
  year={2000}
}

@article{Representation_Learning2013,
  title={Representation Learning: A Review and New Perspectives},
  author={Yoshua Bengio and Aaron Courville and Pascal Vincent},
  journal=TPMAI,
  year={2013},
  volume={35},
  pages={1798-1828}
}

@article{Disentangled_Representations2021,
  title={A Tutorial on Learning Disentangled Representations in the Imaging Domain},
  author={Xiao Liu and Pedro Sanchez and Spyridon Thermos and Alison Q. O'Neil and Sotirios A. Tsaftaris},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.12043}
}

@inproceedings{Counterfactual_Generative_Networks,
  title={Counterfactual Generative Networks},
  author={Axel Sauer and Andreas Geiger},
  year={2021},
  booktitle = ICLR
}


@inproceedings{Generative_Interventions_for_Causal_Learning,
  title={Generative Interventions for Causal Learning},
  author={Chengzhi Mao and Augustine Cha and Amogh Gupta and Hong Wang and Junfeng Yang and Carl Vondrick},
  booktitle=CVPR,
  year={2021}
}


@inproceedings{NOTEARS,
 author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep K and Xing, Eric P},
 booktitle = NIPS,
 pages = {9472--9483},
 publisher = {Curran Associates, Inc.},
 title = {DAGs with NO TEARS: Continuous Optimization for Structure Learning},
 volume = {31},
 year = {2018}
}


@inproceedings{DAGGNN,
	Author = {Yu, Yue and Chen, Jie and Gao, Tian and Yu, Mo},
	Booktitle = ICML,
	Pages = {7154--7163},
	Publisher = {PMLR},
	Title = {{DAG}-{GNN}: {DAG} Structure Learning with Graph Neural Networks},
	Url = {https://proceedings.mlr.press/v97/yu19a.html},
	Volume = {97},
	Year = {2019},
	Bdsk-Url-1 = {https://proceedings.mlr.press/v97/yu19a.html}}


@inproceedings{DARING,
  author = {He, Yue and Cui, Peng and Shen, Zheyan and Xu, Renzhe and Liu, Furui and Jiang, Yong},
  title = {{DARING}: Differentiable Causal Discovery with Residual Independence},
  year = {2021},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3447548.3467439},
  doi = {10.1145/3447548.3467439},
  booktitle = KDD,
  pages = {596–605},
  numpages = {10},
  keywords = {mutual independence, causal discovery, adversarial learning, DARING},
  location = {Virtual Event, Singapore},
  series = {KDD '21}
}

@article{Density_ratio_matching,
  title={Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation},
  author={Masashi Sugiyama and Teruyuki Suzuki and Takafumi Kanamori},
  journal={Annals of the Institute of Statistical Mathematics},
  year={2012},
  volume={64},
  pages={1009-1044},
  publisher={Springer}
}


@inproceedings{FactorVAE,
	Author = {Kim, Hyunjik and Mnih, Andriy},
	Booktitle = {International Conference on Machine Learning (ICML)},
	Pages = {2654--2663},
	Publisher = {PMLR},
	Series = {Proceedings of Machine Learning Research},
	Title = {Disentangling by Factorising},
	Volume = {80},
	Year = {2018}}


@inproceedings{On_the_Role_of_Sparsity_and_DAG_Constraints_for_Learning_Linear_DAGs,
  author = {Ng, Ignavier and Ghassami, AmirEmad and Zhang, Kun},
  booktitle = NIPS,
  editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
  pages = {17943--17954},
  publisher = {Curran Associates, Inc.},
  title = {On the Role of Sparsity and DAG Constraints for Learning Linear DAGs},
  url = {https://proceedings.neurips.cc/paper/2020/file/d04d42cdf14579cd294e5079e0745411-Paper.pdf},
  volume = {33},
  year = {2020}
}

@inproceedings{NOFEARS,
 author = {Wei, Dennis and Gao, Tian and Yu, Yue},
 booktitle = NIPS,
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {3895--3906},
 publisher = {Curran Associates, Inc.},
 title = {DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks},
 url = {https://proceedings.neurips.cc/paper/2020/file/28a7602724ba16600d5ccc644c19bf18-Paper.pdf},
 volume = {33},
 year = {2020}
}

@inproceedings{CASTLE_NIPS,
 author = {Kyono, Trent and Zhang, Yao and van der Schaar, Mihaela},
 booktitle = NIPS,
 pages = {1501--1512},
 publisher = {Curran Associates, Inc.},
 title = {{CASTLE}: Regularization via Auxiliary Causal Graph Discovery},
 url = {https://proceedings.neurips.cc/paper/2020/file/1068bceb19323fe72b2b344ccf85c254-Paper.pdf},
 volume = {33},
 year = {2020}
}

@article{Cai2021OnTR,
  title={On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization},
  author={Ruichu Cai and Weilin Chen and Jie Qiao and Z. Hao},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.02835}
}

@inproceedings{layout2image_cvpr2021,
  title={Context-Aware Layout to Image Generation with Enhanced Object Appearance},
  author={Sen He and Wentong Liao and Michael Ying Yang and Yongxin Yang and Yi-Zhe Song and Bodo Rosenhahn and Tao Xiang},
  booktitle=CVPR,
  year={2021},
  pages     = {15049-15058}
}
% lostgan上面加attn 和 style loss

@article{Sun2021DeepCL,
  title={Deep Consensus Learning},
  author={Weifeng Sun and Tianfu Wu},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.08475}
}
% LostGAN作者后续


@InProceedings{GIRAFFE,
    author    = {Niemeyer, Michael and Geiger, Andreas},
    title     = {{GIRAFFE}: Representing Scenes As Compositional Generative Neural Feature Fields},
    booktitle = CVPR,
    month     = {June},
    year      = {2021},
    pages     = {11453-11464}
}
% cvpr 2021 best

@article{AttrLostGAN,
  title={{AttrLostGAN}: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style},
  author={Stanislav Frolov and Avneesh Sharma and J{\"o}rn Hees and Tushar Karayil and Federico Raue and Andreas R. Dengel},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.13722}
}
% lostgan基础上加attr

@InProceedings{Visual-Relation_Conscious_Image_Generation_from_Structured-Text,
  author="Vo, Duc Minh and Sugimoto, Akihiro", 
  title="Visual-Relation Conscious Image Generation from Structured-Text",
  booktitle=ECCV,
  year="2020",
  publisher="Springer International Publishing",
  pages="290--306",
  isbn="978-3-030-58604-1"
}

@inproceedings{Taming_transformers_for_high-resolution_image_synthesis,
  author          = {Patrick Esser and Robin Rombach and Bj{\"o}rn Ommer},
  title           = {Taming transformers for high-resolution image synthesis},
  booktitle       = cvpr,
  year            = {2021},
  pages           = {12873--12883},
}

@article{LSTM,
  title={Long Short-Term Memory},
  author={Sepp Hochreiter and J{\"u}rgen Schmidhuber},
  journal={Neural Computation},
  year={1997},
  volume={9},
  pages={1735-1780}
}

@inproceedings{GCN,
  title={Semi-Supervised Classification with Graph Convolutional Networks},
  author={Thomas Kipf and Max Welling},
  booktitle=ICLR,
  year={2017}
}


@article{ZHOU202057,
	Abstract = {Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.},
	Author = {Jie Zhou and Ganqu Cui and Shengding Hu and Zhengyan Zhang and Cheng Yang and Zhiyuan Liu and Lifeng Wang and Changcheng Li and Maosong Sun},
	Doi = {https://doi.org/10.1016/j.aiopen.2021.01.001},
	Issn = {2666-6510},
	Journal = {AI Open},
	Keywords = {Deep learning, Graph neural network},
	Pages = {57-81},
	Title = {Graph neural networks: A review of methods and applications},
	Url = {https://www.sciencedirect.com/science/article/pii/S2666651021000012},
	Volume = {1},
	Year = {2020},
	Bdsk-Url-1 = {https://www.sciencedirect.com/science/article/pii/S2666651021000012},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.aiopen.2021.01.001}}

@ARTICLE{A_Comprehensive_Survey_on_Graph_Neural_Networks_TNNLS,  
    author={Wu, Zonghan and Pan, Shirui and Chen, Fengwen and Long, Guodong and Zhang, Chengqi and Yu, Philip S.},
    journal=TNNLS,
     title={A Comprehensive Survey on Graph Neural Networks},
     year={2021},
    volume={32},
    number={1},
    pages={4-24},
    doi={10.1109/TNNLS.2020.2978386}
  }

 @inproceedings{Unbiased_Scene_Graph_Generation_From_Biased_Training,
  author    = {Tang, Kaihua and Niu, Yulei and Huang, Jianqiang and Shi, Jiaxin and Zhang, Hanwang},
  title     = {Unbiased Scene Graph Generation From Biased Training},
  booktitle = CVPR,
  pages = {3716-3725},
  month     = {June},
  year      = {2020}
}

@inproceedings{Recovering_the_Unbiased_Scene_Graphs_from_the_Biased_Ones,
  title   = {Recovering the Unbiased Scene Graphs from the Biased Ones},
  author  = {Meng-Jiun Chiou and Henghui Ding and Hanshu Yan and Changhu Wang and Roger Zimmermann and Jiashi Feng},
  booktitle = ACMMM,
  year    = {2021},
}



@article{ML4C_2110,
  title={{ML4C}: Seeing Causality Through Latent Vicinity},
  author={Haoyue Dai and Rui Ding and Yuanyuan Jiang and Shi Han and Dongmei Zhang},
  year={2021},
  journal={ArXiv},
  volume={abs/2110.00637}
  }
  % 在supervised learning 里面加入causality作为辅助，证明有效
  
  @inproceedings{couterfactual_VQA_2021,
  title={Counterfactual Samples Synthesizing and Training for Robust Visual Question Answering},
  author={Long Chen and Yuhang Zheng and Yulei Niu and Hanwang Zhang and Jun Xiao},
  journal={ArXiv},
  volume={abs/2110.01013},
  year={2021}
}

@inproceedings{SceneGG_first,
  title={Scene Graph Generation by Iterative Message Passing},
  author={Danfei Xu and Yuke Zhu and Christopher Bongsoo Choy and Li Fei-Fei},
  booktitle=CVPR,
  year={2017},
  pages={3097-3106}
}

@article{SGG_TNNLS0,
title={Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions},
author={Woo, Sangmin and Noh, Junhyug and Kim, Kangil},
journal={arXiv preprint arXiv:2106.08543},
year={2021}
}

@ARTICLE{SGG_TNNLS1,  
  author={Liu, An-An and Tian, Hongshuo and Xu, Ning and Nie, Weizhi and Zhang, Yongdong and Kankanhalli, Mohan},
  journal=TNNLS,
  title={Toward Region-Aware Attention Learning for Scene Graph Generation},
  year={2021},  
  doi={10.1109/TNNLS.2021.3086066}
  }
%

@ARTICLE{SGG_TNNLS2,  
  author={Ren, Guanghui and Ren, Lejian and Liao, Yue and Liu, Si and Li, Bo and Han, Jizhong and Yan, Shuicheng},
  journal=TNNLS,
  title={Scene Graph Generation With Hierarchical Context},
  year={2021},
  volume={32},
  number={2},  
  pages={909-915},  
  doi={10.1109/TNNLS.2020.2979270}
}
% 考虑spatial correlations between objects in the hierarchical context